"""deliberation node — GPT peer review of Gemini's compliance analysis."""
from __future__ import annotations

from datetime import datetime

from openai import OpenAI

from config import DELIBERATION_MODEL, OPENAI_API_KEY
from prompts.deliberation import DELIBERATION_SYSTEM_PROMPT
from state import AgentMessage, ComplianceState
from tools.image_store import ImageStore


def deliberation(state: ComplianceState, image_store: ImageStore) -> dict:
    """Send compliance analysis + images + code report to GPT for peer review."""
    question = state["question"]
    compliance_analysis = state.get("compliance_analysis", "")
    code_report = state.get("code_report", "")
    image_refs = state.get("image_refs", [])

    if not compliance_analysis:
        return {
            "reviewer_analysis": "",
            "discussion_log": [
                AgentMessage(
                    timestamp=datetime.now().strftime("%H:%M:%S"),
                    agent="reviewer",
                    action="review",
                    summary="No analysis to review.",
                    detail="",
                    evidence_refs=[],
                )
            ],
            "status_message": ["No analysis to review."],
        }

    client = OpenAI(api_key=OPENAI_API_KEY)

    # Build multimodal message
    user_content: list[dict] = [
        {"type": "text", "text": f"USER COMPLIANCE QUESTION: {question}"},
        {"type": "text", "text": f"\n=== LEGAL REQUIREMENTS ===\n{code_report}"},
        {"type": "text", "text": f"\n=== ANALYST'S COMPLIANCE FINDINGS ===\n{compliance_analysis}"},
        {"type": "text", "text": "\nBELOW ARE THE SAME CROPPED IMAGES THE ANALYST EXAMINED:"},
    ]

    for ref in image_refs:
        user_content.append(
            {"type": "text", "text": f"\nImage: {ref['label']}"}
        )
        try:
            user_content.append(image_store.to_openai_base64(ref))
        except Exception as e:
            user_content.append(
                {"type": "text", "text": f"(Could not load image: {e})"}
            )

    user_content.append(
        {"type": "text", "text": "\nPerform your peer review of the compliance determination."}
    )

    response = client.chat.completions.create(
        model=DELIBERATION_MODEL,
        messages=[
            {"role": "system", "content": DELIBERATION_SYSTEM_PROMPT},
            {"role": "user", "content": user_content},
        ],
    )

    review_text = response.choices[0].message.content or ""

    discussion_msg = AgentMessage(
        timestamp=datetime.now().strftime("%H:%M:%S"),
        agent="reviewer",
        action="review",
        summary=f"Peer review complete. {review_text[:100]}...",
        detail=review_text[:1500],
        evidence_refs=[],
    )

    return {
        "reviewer_analysis": review_text,
        "discussion_log": [discussion_msg],
        "status_message": ["Deliberation/peer review complete."],
    }